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As an emerging visualization technology, virtual reality (VR) falls into the dilemma of having great potential but a low adoption degree in the architectural, engineering and construction (AEC) industry. However, few studies paid attention to studying barriers affecting VR’s adoption and their inner mechanisms. This makes AEC users hard to catch the key points for VR’s implementations. This study aims to get a clear structure of these barriers and provide insights for the improvement.

First, 12 major VR-AEC adoption barriers were identified by a systematic literature review and expert interviews (EI). Second, EI and similarity aggregation method were conducted to achieve reliable barrier relationships. Third, interpretive structural modeling was used to establish a multi-level model for barriers. Finally, ten crucial barriers were targeted with a comprehensive strategy framework.

The findings help AEC stakeholders get a thorough understanding of the VR-AEC adoption barriers. Besides, the inner mechanism among barriers is revealed and analyzed, followed by a systematic strategy framework. It is anticipated that users could conduct more effective VR-AEC promotions in the future.

This paper is the first to propose a comprehensive literature review on the VR-AEC adoption barriers. In addition, this paper is novel in building a hierarchy model that explores barriers’ inner mechanism, where structural strategies are proposed.

This study aims to deepen the understanding of the challenges and implications entailed by deploying mobile clinics in conflict zones to reach populations affected by violence and cut off from health-care services.

This research combines an integrated literature review and an instrumental case study. The literature review comprises two targeted reviews to provide insights: one on conflict zones and one on mobile clinics. The case study describes the process and challenges faced throughout a mobile clinic deployment during and after the Iraq War. The data was gathered using mixed methods over a two-year period (2017–2018).

Armed conflicts directly impact the populations’ health and access to health care. Mobile clinic deployments are often used and recommended to provide health-care access to vulnerable populations cut off from health-care services. However, there is a dearth of peer-reviewed literature documenting decision support tools for mobile clinic deployments.

This study highlights the gaps in the literature and provides direction for future research to support the development of valuable insights and decision support tools for practitioners.

The purpose of this paper is to design a new trajectory error compensation method to improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction motion.

A trajectory error compensation method based on admittance-extended Kalman filter (AEKF) error fusion for human–exoskeleton interaction control. The admittance controller is used to calculate the trajectory error adjustment through the feedback human–exoskeleton interaction force, and the actual trajectory error is obtained through the encoder feedback of exoskeleton and the designed trajectory. By using the fusion and prediction characteristics of EKF, the calculated trajectory error adjustment and the actual error are fused to obtain a new trajectory error compensation, which is feedback to the knee exoskeleton controller. This method is designed to be capable of improving the trajectory tracking performance of the knee exoskeleton and enhancing the compliance of knee exoskeleton interaction.

Six volunteers conducted comparative experiments on four different motion frequencies. The experimental results show that this method can effectively improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction.

The AEKF method first uses the data fusion idea to fuse the estimated error with measurement errors, obtaining more accurate trajectory error compensation for the knee exoskeleton motion control. This work provides great benefits for the trajectory tracking performance and compliance of lower limb exoskeletons in human–exoskeleton interaction movements.

With the upgrade of natural language interaction technology, the simulation extension of intelligent voice assistants (IVAs) and the uncertainty of products and services have received more and more attention. However, most of the existing research focuses on investigating the application of theories to explain consumer behavior related to intention to use and adopt IVAs, while ignoring the impact of its privacy issues on consumer resistance. This article especially examines the negative impact of artificial intelligence-based IVAs’ privacy concerns on consumer resistance, and studies the mediating effect of perceived creepiness in the context of privacy cynicism and privacy paradox and the moderating effect of anthropomorphized roles of IVAs and perceived corporate social responsibility (CSR) of IVAs’ companies. The demographic variables are also included.

Based on the theory of human–computer interaction (HCI), this study addresses the consumer privacy concerns of IVAs, builds a model of the influence mechanism on consumer resistance, and then verifies the mediating effect of perceived creepiness and the moderating effect of anthropomorphized roles of IVAs and perceived CSR of IVAs companies. This research explores underlying mechanism with three experiments.

It turns out that consumers’ privacy concerns are related to their resistance to IVAs through perceived creepiness. The servant (vs. partner) anthropomorphized role of IVAs is likely to induce more privacy concerns and in turn higher resistance. At the same time, when the company’s CSR is perceived high, the impact of the concerns of IVAs’ privacy issues on consumer resistance will be weakened, and the intermediary mechanism of perceiving creepiness in HCI and anthropomorphism of new technology are further explained and verified. The differences between different age and gender are also revealed in the study.

The research conclusions have strategic reference significance for enterprises to build the design framework of IVAs and formulate the response strategy of IVAs’ privacy concerns. And it offers implications for researchers and closes the research gap of IVAs from the perspective of innovation resistance.

The purpose of this paper is to study the screen surface parameters of the double deck vibrating screen, in sections, to determine the influence of each part of the screen surface on the screening efficiency of the vibrating screen. Finally, the best screening parameters were calculated to obtain the best screening performance.

In this paper, the discrete element method is used to simulate the process of two-layer subsection screening. Response surface test was used to analyze the influence of various factors and their interactions on screening results. Finally, based on the binomial regression model of screening efficiency, the optimal combination of vibration parameters is calculated.

In the screening process of vibrating screen, due to the different screening environments in each area of the screen surface, the single-layer linear vibrating screen with equal screen surface parameters cannot obtain the best screening performance. Among the single factors, the effect of vibration frequency is the most significant.

To address the issue of single layer linear vibrating screens with equal screen surface parameters being unable to maintain optimal screening performance when handling large amounts of materials. This article proposes a double layer vibrating screen with different screen surface grids and screen surface angles to address the problem of low screening performance of traditional single layer linear vibrating screens.

The paper aims to elucidate effective strategies for promoting the adoption of green technology innovation within the private sector, thereby enhancing the value of public–private partnership (PPP) projects during the operational phase.

Utilizing prospect theory, the paper considers the government and the public as external driving forces. It establishes a tripartite evolutionary game model composed of government regulators, the private sector and the public. The paper uses numerical simulations to explore the evolutionary stable equilibrium strategies and the determinants influencing each stakeholder.

The paper demonstrates that government intervention and public participation substantially promote green technology innovation within the private sector. Major influencing factors encompass the intensity of pollution taxation, governmental information disclosure and public attention. However, an optimal threshold exists for environmental publicity and innovation subsidies, as excessive levels might inhibit technological innovation. Furthermore, within government intervention strategies, compensating the public for their participation costs is essential to circumvent the public's “free-rider” tendencies and encourage active public collaboration in PPP project innovation.

By constructing a tripartite evolutionary game model, the paper comprehensively examines the roles of government intervention and public participation in promoting green technology innovation within the private sector, offering fresh perspectives and strategies for the operational phase of PPP projects.

This paper aims to deepen the understanding of the relationship between global economic uncertainty and price volatility, specifically focusing on commodity, industrial materials and energy price indices as proxies for global inflation, analyzing data from 1997 to 2020.

The dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model is used to study the dynamic relationship between variables over a while.

The results demonstrated a positive relationship between commodity prices and the global economic policy uncertainty (GEPU). Except for 1999–2000 and 2006–2008, the results of the energy price index model were very similar to those of the commodity price index. A predominant positive relationship is observed focusing on the connection between GEPU and the industrial material price index. The results of the pairwise Granger causality reveal a unidirectional relationship between the GEPU – the Global Commodity Price Index – and the GEPU – the Global Industrial Material Price Index. However, there is bidirectional causality between the GEPU – the Global Energy Price Index. In sum, changes in price indices can be driven by GEPU as a political factor indicating unfavorable economic conditions.

This paper provides a deeper understanding of the role of global uncertainty in the global inflation process. It fills the gap in the literature by empirically investigating the dynamic movements of global uncertainty and the three most important groups of prices.

This study aims to explore credit scoring regulations, governance, variables and methods used by peer-to-peer (P2P) lending platforms in key players of the Association of Southeast Asian Nations (ASEAN) region’s P2P, Indonesia, Malaysia and Singapore.

This study explores the P2P Lending characteristics of the three countries using qualitative literature review, interview, focus group discussion and desk research.

This study concludes that the credit scoring variables used by the countries’ companies are almost the same. Key drivers of the differences are countries’ regulations, management/business core value and credit scoring data processing methods.

Ultimately, this research provides a comprehensive view for investors, businesses and researchers on the topic of ASEAN credit scoring governance and will help them navigate the complexities and improve their awareness on the importance of credit scoring governance in P2P lending companies.

This research provides an in-depth perspective on how P2P lending companies, credit scoring governance and regulations in the biggest three countries in Southeast Asia.

Session-based recommendation aims to predict the user's next preference based on the user's recent activities. Although most existing studies consider the global characteristics of items, they only learn the global characteristics of items based on a single connection relationship, which cannot fully capture the complex transformation relationship between items. We believe that multiple relationships between items in learning sessions can improve the performance of session recommendation tasks and the scalability of recommendation models. At the same time, high-quality global features of the item help to explore the potential common preferences of users.

This work proposes a session-based recommendation method with a multi-relation global context–enhanced network to capture this global transition relationship. Specifically, we construct a multi-relation global item graph based on a group of sessions, use a graded attention mechanism to learn different types of connection relations independently and obtain the global feature of the item according to the multi-relation weight.

We did related experiments on three benchmark datasets. The experimental results show that our proposed model is superior to the existing state-of-the-art methods, which verifies the effectiveness of our model.

First, we construct a multi-relation global item graph to learn the complex transition relations of the global context of the item and effectively mine the potential association of items between different sessions. Second, our model effectively improves the scalability of the model by obtaining high-quality item global features and enables some previously unconsidered items to make it onto the candidate list.

The electrical properties of piezoelectric vibrators have a crucial influence on the operating state of ultrasonic motors. In order to solve the problem that the current piezoelectric vibrator generates a large amount of heat during vibration to degrade its performance, which in turn affects the normal operation of ultrasonic motors, this paper prepares a novel piezoelectric vibrator and tests its maximum vibration velocity under the working condition, which is more than twice as much as that of the current commercial PZT-8.

The crystal structures of the samples were analyzed by using an X-ray diffractometer. For microstructure observation, samples were observed by scanning electron microscope (SEM). The quasi-static piezoelectric coefficient meter (ZJ-3AN) was used for piezoelectric measurement. Dielectric properties were measured by utilizing an impedance analyzer (Agilent 4294A) with a laboratory heating unit. Ferroelectric hysteresis loops were obtained using a ferroelectric analyzer (Radiant, Multiferroic 100). A Doppler laser vibrometer (Polytec PSV-300F, Germany) and a power amplifier were used for piezoelectric vibration measurements, during which the temperature rise was determined by an infrared radiation thermometer (Victor 303, China).

The ceramics exhibit enhanced piezoelectric performance at 0.1–0.4 mol% of Yb doping contents. The ceramic of 0.4 mol% Yb reaches the maximal internal bias field and presents a larger mechanical quality factor of 1,692 compared with that of 0.2 mol% Yb-doped ceramic, in spite of a slightly decreased dielectric constant of 439 pC/N, the unit of the piezoelectric constant, which is the ratio of the local charge (pC) to the frontal force (N) and electromechanical coupling coefficient of 0.63. The vibrator with this large mechanical quality factor ceramic displays a vibration velocity of up to 0.81 m/s under the constraint of 20 °C temperature rising, which is much higher than commercial high-power piezoelectric ceramics PZT-8.

The enhanced high-power properties of the piezoelectric vibrator by Yb doping may provide a potential application for the high-performance USM and offer the possibility of long-term stable operation under high power for special equipment like USM. In the subsequent phase of research, the novel PZT-based high-power piezoelectric vibrator can be utilized in the USM, and the motor's performance will be evaluated under aerospace conditions to objectively assess the reliability of the piezoelectric vibrator.